Overview

Dataset statistics

Number of variables28
Number of observations14606
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.8 MiB
Average record size in memory347.0 B

Variable types

Categorical3
Numeric20
DateTime4
Boolean1

Alerts

id has a high cardinality: 14606 distinct valuesHigh cardinality
cons_12m is highly overall correlated with cons_last_month and 2 other fieldsHigh correlation
cons_gas_12m is highly overall correlated with nb_prod_actHigh correlation
cons_last_month is highly overall correlated with cons_12m and 2 other fieldsHigh correlation
forecast_cons_12m is highly overall correlated with cons_12m and 3 other fieldsHigh correlation
forecast_cons_year is highly overall correlated with cons_last_month and 3 other fieldsHigh correlation
forecast_meter_rent_12m is highly overall correlated with forecast_price_energy_off_peak and 3 other fieldsHigh correlation
forecast_price_energy_off_peak is highly overall correlated with forecast_meter_rent_12m and 2 other fieldsHigh correlation
forecast_price_energy_peak is highly overall correlated with forecast_meter_rent_12m and 2 other fieldsHigh correlation
forecast_price_pow_off_peak is highly overall correlated with forecast_meter_rent_12m and 3 other fieldsHigh correlation
imp_cons is highly overall correlated with cons_last_month and 3 other fieldsHigh correlation
margin_gross_pow_ele is highly overall correlated with margin_net_pow_eleHigh correlation
margin_net_pow_ele is highly overall correlated with margin_gross_pow_eleHigh correlation
nb_prod_act is highly overall correlated with cons_gas_12mHigh correlation
net_margin is highly overall correlated with cons_12m and 3 other fieldsHigh correlation
num_years_antig is highly overall correlated with start_yearHigh correlation
pow_max is highly overall correlated with forecast_meter_rent_12m and 3 other fieldsHigh correlation
start_year is highly overall correlated with num_years_antigHigh correlation
churn is highly imbalanced (54.0%)Imbalance
end_year is highly imbalanced (65.5%)Imbalance
net_margin is highly skewed (γ1 = 36.56951466)Skewed
id is uniformly distributedUniform
id has unique valuesUnique
channel_sales has 3725 (25.5%) zerosZeros
cons_gas_12m has 11994 (82.1%) zerosZeros
cons_last_month has 4983 (34.1%) zerosZeros
forecast_cons_12m has 306 (2.1%) zerosZeros
forecast_cons_year has 6148 (42.1%) zerosZeros
forecast_discount_energy has 14094 (96.5%) zerosZeros
forecast_meter_rent_12m has 725 (5.0%) zerosZeros
forecast_price_energy_peak has 7021 (48.1%) zerosZeros
imp_cons has 6169 (42.2%) zerosZeros
margin_gross_pow_ele has 157 (1.1%) zerosZeros
margin_net_pow_ele has 157 (1.1%) zerosZeros
net_margin has 185 (1.3%) zerosZeros

Reproduction

Analysis started2023-07-08 12:42:02.844357
Analysis finished2023-07-08 12:42:49.393034
Duration46.55 seconds
Software versionpandas-profiling vv3.6.2
Download configurationconfig.json

Variables

id
Categorical

HIGH CARDINALITY  UNIFORM  UNIQUE 

Distinct14606
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
24011ae4ebbe3035111d65fa7c15bc57
 
1
2fae70276cd7a4874a2aefcd68d5a184
 
1
a7a9dac0ffc2ad56c66a1b08d53d0e51
 
1
6cfbce2099a4163ea4fe422fb28829ea
 
1
4289e929ecd35a0754e697feb24a091b
 
1
Other values (14601)
14601 

Length

Max length32
Median length32
Mean length32
Min length32

Characters and Unicode

Total characters467392
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14606 ?
Unique (%)100.0%

Sample

1st row24011ae4ebbe3035111d65fa7c15bc57
2nd rowd29c2c54acc38ff3c0614d0a653813dd
3rd row764c75f661154dac3a6c254cd082ea7d
4th rowbba03439a292a1e166f80264c16191cb
5th row149d57cf92fc41cf94415803a877cb4b

Common Values

ValueCountFrequency (%)
24011ae4ebbe3035111d65fa7c15bc57 1
 
< 0.1%
2fae70276cd7a4874a2aefcd68d5a184 1
 
< 0.1%
a7a9dac0ffc2ad56c66a1b08d53d0e51 1
 
< 0.1%
6cfbce2099a4163ea4fe422fb28829ea 1
 
< 0.1%
4289e929ecd35a0754e697feb24a091b 1
 
< 0.1%
1a48dbd5cc49dc74542bd008a684e247 1
 
< 0.1%
ae2699b4f74ce6e5819ee4f16049c03d 1
 
< 0.1%
4be734763fa4f51371a161491ce4a8a8 1
 
< 0.1%
0659a0dae786fed9915a37df88302a83 1
 
< 0.1%
c961b41f4f388c1bd8d126c40d9703f5 1
 
< 0.1%
Other values (14596) 14596
99.9%

Length

2023-07-08T18:12:49.552631image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
24011ae4ebbe3035111d65fa7c15bc57 1
 
< 0.1%
26c7bba7d51f86a16109de505bcd4f52 1
 
< 0.1%
74ff037708f036de5745ce34d8d9d4df 1
 
< 0.1%
21860c2ff2d5df75503b230ce629c253 1
 
< 0.1%
764c75f661154dac3a6c254cd082ea7d 1
 
< 0.1%
bba03439a292a1e166f80264c16191cb 1
 
< 0.1%
149d57cf92fc41cf94415803a877cb4b 1
 
< 0.1%
1aa498825382410b098937d65c4ec26d 1
 
< 0.1%
7ab4bf4878d8f7661dfc20e9b8e18011 1
 
< 0.1%
01495c955be7ec5e7f3203406785aae0 1
 
< 0.1%
Other values (14596) 14596
99.9%

Most occurring characters

ValueCountFrequency (%)
6 29453
 
6.3%
e 29432
 
6.3%
7 29400
 
6.3%
4 29381
 
6.3%
2 29375
 
6.3%
b 29313
 
6.3%
3 29297
 
6.3%
1 29257
 
6.3%
c 29210
 
6.2%
a 29169
 
6.2%
Other values (6) 174105
37.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 292239
62.5%
Lowercase Letter 175153
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 29453
10.1%
7 29400
10.1%
4 29381
10.1%
2 29375
10.1%
3 29297
10.0%
1 29257
10.0%
8 29153
10.0%
0 29090
10.0%
9 29012
9.9%
5 28821
9.9%
Lowercase Letter
ValueCountFrequency (%)
e 29432
16.8%
b 29313
16.7%
c 29210
16.7%
a 29169
16.7%
d 29137
16.6%
f 28892
16.5%

Most occurring scripts

ValueCountFrequency (%)
Common 292239
62.5%
Latin 175153
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
6 29453
10.1%
7 29400
10.1%
4 29381
10.1%
2 29375
10.1%
3 29297
10.0%
1 29257
10.0%
8 29153
10.0%
0 29090
10.0%
9 29012
9.9%
5 28821
9.9%
Latin
ValueCountFrequency (%)
e 29432
16.8%
b 29313
16.7%
c 29210
16.7%
a 29169
16.7%
d 29137
16.6%
f 28892
16.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 467392
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 29453
 
6.3%
e 29432
 
6.3%
7 29400
 
6.3%
4 29381
 
6.3%
2 29375
 
6.3%
b 29313
 
6.3%
3 29297
 
6.3%
1 29257
 
6.3%
c 29210
 
6.2%
a 29169
 
6.2%
Other values (6) 174105
37.3%

channel_sales
Real number (ℝ)

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2669451
Minimum0
Maximum7
Zeros3725
Zeros (%)25.5%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2023-07-08T18:12:49.930619image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q34
95-th percentile7
Maximum7
Range7
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.1832229
Coefficient of variation (CV)0.66827658
Kurtosis-0.85117678
Mean3.2669451
Median Absolute Deviation (MAD)1
Skewness-0.31405343
Sum47717
Variance4.7664622
MonotonicityNot monotonic
2023-07-08T18:12:50.016389image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4 6754
46.2%
0 3725
25.5%
5 1843
 
12.6%
7 1375
 
9.4%
2 893
 
6.1%
6 11
 
0.1%
1 3
 
< 0.1%
3 2
 
< 0.1%
ValueCountFrequency (%)
0 3725
25.5%
1 3
 
< 0.1%
2 893
 
6.1%
3 2
 
< 0.1%
4 6754
46.2%
5 1843
 
12.6%
6 11
 
0.1%
7 1375
 
9.4%
ValueCountFrequency (%)
7 1375
 
9.4%
6 11
 
0.1%
5 1843
 
12.6%
4 6754
46.2%
3 2
 
< 0.1%
2 893
 
6.1%
1 3
 
< 0.1%
0 3725
25.5%

cons_12m
Real number (ℝ)

Distinct11065
Distinct (%)75.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159220.29
Minimum0
Maximum6207104
Zeros117
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-07-08T18:12:50.137067image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1512.25
Q15674.75
median14115.5
Q340763.75
95-th percentile913771.75
Maximum6207104
Range6207104
Interquartile range (IQR)35089

Descriptive statistics

Standard deviation573465.26
Coefficient of variation (CV)3.6017098
Kurtosis42.689777
Mean159220.29
Median Absolute Deviation (MAD)10669
Skewness5.9973081
Sum2.3255715 × 109
Variance3.2886241 × 1011
MonotonicityNot monotonic
2023-07-08T18:12:50.271708image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 117
 
0.8%
2882597 27
 
0.2%
3329244 24
 
0.2%
1743025 18
 
0.1%
3926060 18
 
0.1%
6207104 18
 
0.1%
1722179 17
 
0.1%
2288838 17
 
0.1%
2503923 16
 
0.1%
963288 16
 
0.1%
Other values (11055) 14318
98.0%
ValueCountFrequency (%)
0 117
0.8%
1 2
 
< 0.1%
2 2
 
< 0.1%
3 4
 
< 0.1%
4 2
 
< 0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
7 3
 
< 0.1%
9 2
 
< 0.1%
10 4
 
< 0.1%
ValueCountFrequency (%)
6207104 18
0.1%
5731448 14
0.1%
5322441 1
 
< 0.1%
5161456 4
 
< 0.1%
4939487 4
 
< 0.1%
4406520 14
0.1%
4306656 9
0.1%
4199490 5
 
< 0.1%
4100379 13
0.1%
4012564 2
 
< 0.1%

cons_gas_12m
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2112
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28092.375
Minimum0
Maximum4154590
Zeros11994
Zeros (%)82.1%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-07-08T18:12:50.405350image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile75854
Maximum4154590
Range4154590
Interquartile range (IQR)0

Descriptive statistics

Standard deviation162973.06
Coefficient of variation (CV)5.8013271
Kurtosis126.33363
Mean28092.375
Median Absolute Deviation (MAD)0
Skewness9.59753
Sum4.1031723 × 108
Variance2.6560218 × 1010
MonotonicityNot monotonic
2023-07-08T18:12:50.558939image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 11994
82.1%
976731 27
 
0.2%
867921 24
 
0.2%
41532 18
 
0.1%
1959386 18
 
0.1%
1192414 17
 
0.1%
475413 16
 
0.1%
468369 15
 
0.1%
1337056 14
 
0.1%
187578 13
 
0.1%
Other values (2102) 2450
 
16.8%
ValueCountFrequency (%)
0 11994
82.1%
11 7
 
< 0.1%
12 2
 
< 0.1%
21 2
 
< 0.1%
32 1
 
< 0.1%
35 2
 
< 0.1%
36 1
 
< 0.1%
41 1
 
< 0.1%
43 2
 
< 0.1%
46 1
 
< 0.1%
ValueCountFrequency (%)
4154590 2
 
< 0.1%
2813019 2
 
< 0.1%
2055098 2
 
< 0.1%
1959386 18
0.1%
1860052 4
 
< 0.1%
1859491 3
 
< 0.1%
1813943 1
 
< 0.1%
1711930 1
 
< 0.1%
1653924 2
 
< 0.1%
1542867 1
 
< 0.1%

cons_last_month
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4751
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16090.27
Minimum0
Maximum771203
Zeros4983
Zeros (%)34.1%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-07-08T18:12:50.710534image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median792.5
Q33383
95-th percentile82161.5
Maximum771203
Range771203
Interquartile range (IQR)3383

Descriptive statistics

Standard deviation64364.196
Coefficient of variation (CV)4.0001937
Kurtosis47.762991
Mean16090.27
Median Absolute Deviation (MAD)792.5
Skewness6.391407
Sum2.3501448 × 108
Variance4.1427498 × 109
MonotonicityNot monotonic
2023-07-08T18:12:50.858138image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4983
34.1%
382647 27
 
0.2%
509826 24
 
0.2%
558120 18
 
0.1%
469210 18
 
0.1%
106161 18
 
0.1%
181187 17
 
0.1%
237044 17
 
0.1%
54281 16
 
0.1%
313018 16
 
0.1%
Other values (4741) 9452
64.7%
ValueCountFrequency (%)
0 4983
34.1%
1 13
 
0.1%
2 5
 
< 0.1%
3 5
 
< 0.1%
4 4
 
< 0.1%
5 5
 
< 0.1%
6 2
 
< 0.1%
7 5
 
< 0.1%
8 5
 
< 0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
771203 14
0.1%
760727 1
 
< 0.1%
612247 1
 
< 0.1%
558120 18
0.1%
509826 24
0.2%
507598 14
0.1%
479030 9
 
0.1%
469898 2
 
< 0.1%
469210 18
0.1%
456462 5
 
< 0.1%
Distinct1796
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Memory size114.2 KiB
Minimum2003-05-09 00:00:00
Maximum2014-09-01 00:00:00
2023-07-08T18:12:51.001754image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:51.142378image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct368
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size114.2 KiB
Minimum2016-01-28 00:00:00
Maximum2017-06-13 00:00:00
2023-07-08T18:12:51.278045image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:51.407696image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct2129
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Memory size114.2 KiB
Minimum2003-05-09 00:00:00
Maximum2016-01-29 00:00:00
2023-07-08T18:12:51.526384image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:51.666041image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct386
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size114.2 KiB
Minimum2013-06-26 00:00:00
Maximum2016-01-28 00:00:00
2023-07-08T18:12:51.805670image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:51.945295image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

forecast_cons_12m
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13993
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1868.6149
Minimum0
Maximum82902.83
Zeros306
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-07-08T18:12:52.087886image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile84.9175
Q1494.995
median1112.875
Q32401.79
95-th percentile6127.095
Maximum82902.83
Range82902.83
Interquartile range (IQR)1906.795

Descriptive statistics

Standard deviation2387.5715
Coefficient of variation (CV)1.2777226
Kurtosis147.42668
Mean1868.6149
Median Absolute Deviation (MAD)752.955
Skewness7.1558526
Sum27292989
Variance5700497.8
MonotonicityNot monotonic
2023-07-08T18:12:52.217561image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 306
 
2.1%
0.15 6
 
< 0.1%
415.14 4
 
< 0.1%
0.45 3
 
< 0.1%
1539.37 3
 
< 0.1%
651.21 3
 
< 0.1%
442.74 3
 
< 0.1%
335.5 3
 
< 0.1%
0.3 3
 
< 0.1%
303.93 3
 
< 0.1%
Other values (13983) 14269
97.7%
ValueCountFrequency (%)
0 306
2.1%
0.1 1
 
< 0.1%
0.15 6
 
< 0.1%
0.18 1
 
< 0.1%
0.2 1
 
< 0.1%
0.3 3
 
< 0.1%
0.32 1
 
< 0.1%
0.33 1
 
< 0.1%
0.42 2
 
< 0.1%
0.45 3
 
< 0.1%
ValueCountFrequency (%)
82902.83 1
< 0.1%
61357.17 1
< 0.1%
48412.58 1
< 0.1%
35789.29 1
< 0.1%
35312.21 1
< 0.1%
32174.47 1
< 0.1%
31347.11 1
< 0.1%
30533.99 1
< 0.1%
28375.76 1
< 0.1%
27618.39 1
< 0.1%

forecast_cons_year
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct4218
Distinct (%)28.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1399.7629
Minimum0
Maximum175375
Zeros6148
Zeros (%)42.1%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-07-08T18:12:52.339214image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median314
Q31745.75
95-th percentile5968.75
Maximum175375
Range175375
Interquartile range (IQR)1745.75

Descriptive statistics

Standard deviation3247.7863
Coefficient of variation (CV)2.3202403
Kurtosis653.73441
Mean1399.7629
Median Absolute Deviation (MAD)314
Skewness16.58799
Sum20444937
Variance10548116
MonotonicityNot monotonic
2023-07-08T18:12:52.474876image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6148
42.1%
1 13
 
0.1%
8 13
 
0.1%
7 11
 
0.1%
453 11
 
0.1%
2 9
 
0.1%
524 9
 
0.1%
310 9
 
0.1%
420 9
 
0.1%
173 9
 
0.1%
Other values (4208) 8365
57.3%
ValueCountFrequency (%)
0 6148
42.1%
1 13
 
0.1%
2 9
 
0.1%
3 5
 
< 0.1%
4 8
 
0.1%
5 7
 
< 0.1%
6 5
 
< 0.1%
7 11
 
0.1%
8 13
 
0.1%
9 3
 
< 0.1%
ValueCountFrequency (%)
175375 1
< 0.1%
79127 1
< 0.1%
70180 1
< 0.1%
66643 1
< 0.1%
63969 1
< 0.1%
59460 1
< 0.1%
51604 1
< 0.1%
51336 1
< 0.1%
50106 1
< 0.1%
46491 1
< 0.1%

forecast_discount_energy
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.966726
Minimum0
Maximum30
Zeros14094
Zeros (%)96.5%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-07-08T18:12:52.585610image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum30
Range30
Interquartile range (IQR)0

Descriptive statistics

Standard deviation5.1082887
Coefficient of variation (CV)5.2841122
Kurtosis24.854712
Mean0.966726
Median Absolute Deviation (MAD)0
Skewness5.1550983
Sum14120
Variance26.094613
MonotonicityNot monotonic
2023-07-08T18:12:52.674374image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 14094
96.5%
30 260
 
1.8%
28 102
 
0.7%
24 83
 
0.6%
22 47
 
0.3%
25 7
 
< 0.1%
26 5
 
< 0.1%
19 2
 
< 0.1%
17 2
 
< 0.1%
23 2
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
0 14094
96.5%
5 1
 
< 0.1%
10 1
 
< 0.1%
17 2
 
< 0.1%
19 2
 
< 0.1%
22 47
 
0.3%
23 2
 
< 0.1%
24 83
 
0.6%
25 7
 
< 0.1%
26 5
 
< 0.1%
ValueCountFrequency (%)
30 260
1.8%
28 102
 
0.7%
26 5
 
< 0.1%
25 7
 
< 0.1%
24 83
 
0.6%
23 2
 
< 0.1%
22 47
 
0.3%
19 2
 
< 0.1%
17 2
 
< 0.1%
10 1
 
< 0.1%

forecast_meter_rent_12m
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3528
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.086871
Minimum0
Maximum599.31
Zeros725
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-07-08T18:12:52.792058image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.3325
Q116.18
median18.795
Q3131.03
95-th percentile145.72
Maximum599.31
Range599.31
Interquartile range (IQR)114.85

Descriptive statistics

Standard deviation66.165783
Coefficient of variation (CV)1.0488043
Kurtosis4.4915214
Mean63.086871
Median Absolute Deviation (MAD)9.315
Skewness1.5051479
Sum921446.84
Variance4377.9108
MonotonicityNot monotonic
2023-07-08T18:12:52.926670image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 725
 
5.0%
131.76 238
 
1.6%
18.32 162
 
1.1%
18.37 131
 
0.9%
129.61 103
 
0.7%
15.98 100
 
0.7%
131.38 78
 
0.5%
18.47 77
 
0.5%
16.26 76
 
0.5%
18.42 74
 
0.5%
Other values (3518) 12842
87.9%
ValueCountFrequency (%)
0 725
5.0%
0.09 1
 
< 0.1%
0.18 1
 
< 0.1%
0.24 1
 
< 0.1%
0.27 1
 
< 0.1%
0.31 1
 
< 0.1%
0.33 1
 
< 0.1%
0.34 2
 
< 0.1%
0.35 2
 
< 0.1%
0.36 6
 
< 0.1%
ValueCountFrequency (%)
599.31 5
< 0.1%
585.62 1
 
< 0.1%
562.13 1
 
< 0.1%
552.9 1
 
< 0.1%
548.41 1
 
< 0.1%
439.67 1
 
< 0.1%
434.04 1
 
< 0.1%
407.98 1
 
< 0.1%
407.97 9
0.1%
406.45 1
 
< 0.1%
Distinct516
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13728327
Minimum0
Maximum0.273963
Zeros22
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-07-08T18:12:53.058318image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.11293
Q10.11634
median0.143166
Q30.146348
95-th percentile0.166178
Maximum0.273963
Range0.273963
Interquartile range (IQR)0.030008

Descriptive statistics

Standard deviation0.024622862
Coefficient of variation (CV)0.17935808
Kurtosis8.3645386
Mean0.13728327
Median Absolute Deviation (MAD)0.019738
Skewness-0.11958602
Sum2005.1594
Variance0.00060628535
MonotonicityNot monotonic
2023-07-08T18:12:53.186974image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.145711 933
 
6.4%
0.144902 732
 
5.0%
0.115174 726
 
5.0%
0.146694 644
 
4.4%
0.115237 595
 
4.1%
0.11691 449
 
3.1%
0.1169 406
 
2.8%
0.146348 339
 
2.3%
0.143166 335
 
2.3%
0.116509 308
 
2.1%
Other values (506) 9139
62.6%
ValueCountFrequency (%)
0 22
 
0.2%
0.0006 66
0.5%
0.000901 6
 
< 0.1%
0.092453 73
0.5%
0.094486 1
 
< 0.1%
0.095022 2
 
< 0.1%
0.095061 1
 
< 0.1%
0.095558 1
 
< 0.1%
0.095919 1
 
< 0.1%
0.096095 1
 
< 0.1%
ValueCountFrequency (%)
0.273963 18
0.1%
0.273957 2
 
< 0.1%
0.272981 21
0.1%
0.272972 1
 
< 0.1%
0.245926 8
 
0.1%
0.245347 6
 
< 0.1%
0.237776 11
 
0.1%
0.236794 4
 
< 0.1%
0.236291 3
 
< 0.1%
0.229272 34
0.2%

forecast_price_energy_peak
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct329
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.050490767
Minimum0
Maximum0.195975
Zeros7021
Zeros (%)48.1%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-07-08T18:12:53.317624image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.084138
Q30.098837
95-th percentile0.10175
Maximum0.195975
Range0.195975
Interquartile range (IQR)0.098837

Descriptive statistics

Standard deviation0.049036507
Coefficient of variation (CV)0.97119751
Kurtosis-1.8907547
Mean0.050490767
Median Absolute Deviation (MAD)0.0311955
Skewness-0.014331428
Sum737.46815
Variance0.002404579
MonotonicityNot monotonic
2023-07-08T18:12:53.459245image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7021
48.1%
0.098837 722
 
4.9%
0.100123 596
 
4.1%
0.100015 473
 
3.2%
0.100572 445
 
3.0%
0.101397 308
 
2.1%
0.103487 288
 
2.0%
0.087381 169
 
1.2%
0.086803 159
 
1.1%
0.099419 153
 
1.0%
Other values (319) 4272
29.2%
ValueCountFrequency (%)
0 7021
48.1%
0.076592 1
 
< 0.1%
0.077124 1
 
< 0.1%
0.078125 1
 
< 0.1%
0.078641 2
 
< 0.1%
0.078859 1
 
< 0.1%
0.079221 2
 
< 0.1%
0.079281 2
 
< 0.1%
0.079771 1
 
< 0.1%
0.079799 1
 
< 0.1%
ValueCountFrequency (%)
0.195975 1
 
< 0.1%
0.168092 6
 
< 0.1%
0.168032 7
 
< 0.1%
0.146676 8
 
0.1%
0.136336 59
0.4%
0.13608 1
 
< 0.1%
0.135761 5
 
< 0.1%
0.135732 35
0.2%
0.135182 3
 
< 0.1%
0.134604 2
 
< 0.1%
Distinct41
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.130056
Minimum0
Maximum59.266378
Zeros94
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-07-08T18:12:53.595880image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile40.606701
Q140.606701
median44.311378
Q344.311378
95-th percentile46.305378
Maximum59.266378
Range59.266378
Interquartile range (IQR)3.704677

Descriptive statistics

Standard deviation4.4859882
Coefficient of variation (CV)0.10401072
Kurtosis54.708041
Mean43.130056
Median Absolute Deviation (MAD)0.9969996
Skewness-4.998772
Sum629957.59
Variance20.12409
MonotonicityNot monotonic
2023-07-08T18:12:53.724536image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
44.31137796 6933
47.5%
40.606701 4651
31.8%
45.80687796 697
 
4.8%
46.30537836 615
 
4.2%
45.30837756 419
 
2.9%
41.1052014 302
 
2.1%
40.9390266 237
 
1.6%
41.2713642 160
 
1.1%
58.99595196 118
 
0.8%
0 94
 
0.6%
Other values (31) 380
 
2.6%
ValueCountFrequency (%)
0 94
 
0.6%
35.55576792 1
 
< 0.1%
37.929294 2
 
< 0.1%
40.606701 4651
31.8%
40.728885 31
 
0.2%
40.9390266 237
 
1.6%
41.1052014 302
 
2.1%
41.1067014 1
 
< 0.1%
41.2713642 160
 
1.1%
41.2718682 1
 
< 0.1%
ValueCountFrequency (%)
59.26637796 42
 
0.3%
59.17346796 76
0.5%
59.05128396 1
 
< 0.1%
58.99595196 118
0.8%
53.28437796 14
 
0.1%
47.80087836 8
 
0.1%
47.30687796 2
 
< 0.1%
47.30237796 10
 
0.1%
46.80687756 1
 
< 0.1%
46.80387756 6
 
< 0.1%

has_gas
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size14.4 KiB
False
11955 
True
2651 
ValueCountFrequency (%)
False 11955
81.8%
True 2651
 
18.2%
2023-07-08T18:12:53.866500image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

imp_cons
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7752
Distinct (%)53.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.7869
Minimum0
Maximum15042.79
Zeros6169
Zeros (%)42.2%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-07-08T18:12:53.971191image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median37.395
Q3193.98
95-th percentile638.8175
Maximum15042.79
Range15042.79
Interquartile range (IQR)193.98

Descriptive statistics

Standard deviation341.36937
Coefficient of variation (CV)2.2342843
Kurtosis380.8937
Mean152.7869
Median Absolute Deviation (MAD)37.395
Skewness13.198799
Sum2231605.4
Variance116533.04
MonotonicityNot monotonic
2023-07-08T18:12:54.106854image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6169
42.2%
0.3 5
 
< 0.1%
0.1 5
 
< 0.1%
34.53 4
 
< 0.1%
26.26 4
 
< 0.1%
42.04 4
 
< 0.1%
126.6 4
 
< 0.1%
26.51 4
 
< 0.1%
117.18 4
 
< 0.1%
0.15 4
 
< 0.1%
Other values (7742) 8399
57.5%
ValueCountFrequency (%)
0 6169
42.2%
0.06 1
 
< 0.1%
0.09 2
 
< 0.1%
0.1 5
 
< 0.1%
0.14 1
 
< 0.1%
0.15 4
 
< 0.1%
0.17 2
 
< 0.1%
0.24 1
 
< 0.1%
0.27 1
 
< 0.1%
0.28 2
 
< 0.1%
ValueCountFrequency (%)
15042.79 1
< 0.1%
9682.89 1
< 0.1%
8732.6 1
< 0.1%
8254.16 1
< 0.1%
6787.12 1
< 0.1%
5836.49 1
< 0.1%
5343.76 1
< 0.1%
5311.97 1
< 0.1%
5019.25 1
< 0.1%
4925.36 1
< 0.1%

margin_gross_pow_ele
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2391
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.565121
Minimum0
Maximum374.64
Zeros157
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-07-08T18:12:54.239560image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.56
Q114.28
median21.64
Q329.88
95-th percentile51.72
Maximum374.64
Range374.64
Interquartile range (IQR)15.6

Descriptive statistics

Standard deviation20.231172
Coefficient of variation (CV)0.82357305
Kurtosis35.892607
Mean24.565121
Median Absolute Deviation (MAD)8.12
Skewness4.4726321
Sum358798.16
Variance409.30031
MonotonicityNot monotonic
2023-07-08T18:12:54.375196image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.04 258
 
1.8%
33.12 238
 
1.6%
29.76 170
 
1.2%
34.68 161
 
1.1%
0 157
 
1.1%
16.92 156
 
1.1%
23.76 156
 
1.1%
10.08 151
 
1.0%
19.2 141
 
1.0%
14.64 135
 
0.9%
Other values (2381) 12883
88.2%
ValueCountFrequency (%)
0 157
1.1%
0.03 1
 
< 0.1%
0.12 125
0.9%
0.24 16
 
0.1%
0.36 7
 
< 0.1%
0.48 1
 
< 0.1%
0.6 1
 
< 0.1%
0.64 7
 
< 0.1%
0.66 1
 
< 0.1%
0.68 3
 
< 0.1%
ValueCountFrequency (%)
374.64 1
< 0.1%
314.76 1
< 0.1%
299.64 1
< 0.1%
248.64 1
< 0.1%
225.12 2
< 0.1%
224.89 1
< 0.1%
224.64 1
< 0.1%
219.88 1
< 0.1%
214.35 1
< 0.1%
214.14 1
< 0.1%

margin_net_pow_ele
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2391
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.562517
Minimum0
Maximum374.64
Zeros157
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-07-08T18:12:54.509831image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.56
Q114.28
median21.64
Q329.88
95-th percentile51.72
Maximum374.64
Range374.64
Interquartile range (IQR)15.6

Descriptive statistics

Standard deviation20.23028
Coefficient of variation (CV)0.82362404
Kurtosis35.901232
Mean24.562517
Median Absolute Deviation (MAD)8.12
Skewness4.4733258
Sum358760.13
Variance409.26422
MonotonicityNot monotonic
2023-07-08T18:12:54.647468image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.04 258
 
1.8%
33.12 238
 
1.6%
29.76 170
 
1.2%
34.68 161
 
1.1%
0 157
 
1.1%
16.92 156
 
1.1%
23.76 156
 
1.1%
10.08 151
 
1.0%
19.2 141
 
1.0%
14.64 135
 
0.9%
Other values (2381) 12883
88.2%
ValueCountFrequency (%)
0 157
1.1%
0.03 1
 
< 0.1%
0.12 125
0.9%
0.24 16
 
0.1%
0.36 7
 
< 0.1%
0.48 1
 
< 0.1%
0.6 1
 
< 0.1%
0.64 7
 
< 0.1%
0.66 1
 
< 0.1%
0.68 3
 
< 0.1%
ValueCountFrequency (%)
374.64 1
< 0.1%
314.76 1
< 0.1%
299.64 1
< 0.1%
248.64 1
< 0.1%
225.12 2
< 0.1%
224.89 1
< 0.1%
224.64 1
< 0.1%
219.88 1
< 0.1%
214.35 1
< 0.1%
214.14 1
< 0.1%

nb_prod_act
Real number (ℝ)

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2923456
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-07-08T18:12:54.757148image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum32
Range31
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.70977352
Coefficient of variation (CV)0.54921339
Kurtosis258.95725
Mean1.2923456
Median Absolute Deviation (MAD)0
Skewness8.6368779
Sum18876
Variance0.50377845
MonotonicityNot monotonic
2023-07-08T18:12:54.844935image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1 11431
78.3%
2 2445
 
16.7%
3 523
 
3.6%
4 150
 
1.0%
5 31
 
0.2%
9 11
 
0.1%
6 8
 
0.1%
8 4
 
< 0.1%
10 2
 
< 0.1%
32 1
 
< 0.1%
ValueCountFrequency (%)
1 11431
78.3%
2 2445
 
16.7%
3 523
 
3.6%
4 150
 
1.0%
5 31
 
0.2%
6 8
 
0.1%
8 4
 
< 0.1%
9 11
 
0.1%
10 2
 
< 0.1%
32 1
 
< 0.1%
ValueCountFrequency (%)
32 1
 
< 0.1%
10 2
 
< 0.1%
9 11
 
0.1%
8 4
 
< 0.1%
6 8
 
0.1%
5 31
 
0.2%
4 150
 
1.0%
3 523
 
3.6%
2 2445
 
16.7%
1 11431
78.3%

net_margin
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct11965
Distinct (%)81.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean189.26452
Minimum0
Maximum24570.65
Zeros185
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-07-08T18:12:54.968609image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.16
Q150.7125
median112.53
Q3243.0975
95-th percentile587.685
Maximum24570.65
Range24570.65
Interquartile range (IQR)192.385

Descriptive statistics

Standard deviation311.79813
Coefficient of variation (CV)1.6474198
Kurtosis2642.9653
Mean189.26452
Median Absolute Deviation (MAD)75.32
Skewness36.569515
Sum2764397.6
Variance97218.074
MonotonicityNot monotonic
2023-07-08T18:12:55.108239image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 185
 
1.3%
0.49 6
 
< 0.1%
35.98 5
 
< 0.1%
57.37 5
 
< 0.1%
86.52 5
 
< 0.1%
56.33 5
 
< 0.1%
0.01 5
 
< 0.1%
234.83 4
 
< 0.1%
78.22 4
 
< 0.1%
33.8 4
 
< 0.1%
Other values (11955) 14378
98.4%
ValueCountFrequency (%)
0 185
1.3%
0.01 5
 
< 0.1%
0.02 4
 
< 0.1%
0.03 3
 
< 0.1%
0.04 3
 
< 0.1%
0.05 3
 
< 0.1%
0.06 1
 
< 0.1%
0.07 1
 
< 0.1%
0.08 1
 
< 0.1%
0.09 2
 
< 0.1%
ValueCountFrequency (%)
24570.65 1
< 0.1%
10203.5 1
< 0.1%
4346.37 1
< 0.1%
4305.79 1
< 0.1%
3768.16 1
< 0.1%
3407.65 1
< 0.1%
3403.27 1
< 0.1%
3323.02 1
< 0.1%
3215.03 1
< 0.1%
2711.19 1
< 0.1%

num_years_antig
Real number (ℝ)

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9978091
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-07-08T18:12:55.214950image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q14
median5
Q36
95-th percentile7
Maximum13
Range12
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6117493
Coefficient of variation (CV)0.32249116
Kurtosis4.0781495
Mean4.9978091
Median Absolute Deviation (MAD)1
Skewness1.4462138
Sum72998
Variance2.5977357
MonotonicityNot monotonic
2023-07-08T18:12:55.305711image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
6 4769
32.7%
4 3982
27.3%
3 2433
16.7%
5 2317
15.9%
7 509
 
3.5%
11 185
 
1.3%
12 110
 
0.8%
8 103
 
0.7%
9 92
 
0.6%
10 81
 
0.6%
Other values (3) 25
 
0.2%
ValueCountFrequency (%)
1 1
 
< 0.1%
2 11
 
0.1%
3 2433
16.7%
4 3982
27.3%
5 2317
15.9%
6 4769
32.7%
7 509
 
3.5%
8 103
 
0.7%
9 92
 
0.6%
10 81
 
0.6%
ValueCountFrequency (%)
13 13
 
0.1%
12 110
 
0.8%
11 185
 
1.3%
10 81
 
0.6%
9 92
 
0.6%
8 103
 
0.7%
7 509
 
3.5%
6 4769
32.7%
5 2317
15.9%
4 3982
27.3%

origin_up
Real number (ℝ)

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1788991
Minimum0
Maximum5
Zeros64
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2023-07-08T18:12:55.395470image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q12
median3
Q34
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.88780149
Coefficient of variation (CV)0.27927955
Kurtosis-1.0045517
Mean3.1788991
Median Absolute Deviation (MAD)1
Skewness-0.5068071
Sum46431
Variance0.78819149
MonotonicityNot monotonic
2023-07-08T18:12:55.481212image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 7097
48.6%
2 4294
29.4%
3 3148
21.6%
0 64
 
0.4%
5 2
 
< 0.1%
1 1
 
< 0.1%
ValueCountFrequency (%)
0 64
 
0.4%
1 1
 
< 0.1%
2 4294
29.4%
3 3148
21.6%
4 7097
48.6%
5 2
 
< 0.1%
ValueCountFrequency (%)
5 2
 
< 0.1%
4 7097
48.6%
3 3148
21.6%
2 4294
29.4%
1 1
 
< 0.1%
0 64
 
0.4%

pow_max
Real number (ℝ)

Distinct698
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.135136
Minimum3.3
Maximum320
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-07-08T18:12:55.596929image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum3.3
5-th percentile10.39
Q112.5
median13.856
Q319.1725
95-th percentile41.5
Maximum320
Range316.7
Interquartile range (IQR)6.6725

Descriptive statistics

Standard deviation13.534743
Coefficient of variation (CV)0.74632711
Kurtosis59.202563
Mean18.135136
Median Absolute Deviation (MAD)3.056
Skewness5.7867849
Sum264881.79
Variance183.18928
MonotonicityNot monotonic
2023-07-08T18:12:55.727551image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.2 2124
 
14.5%
10.392 2000
 
13.7%
13.856 1504
 
10.3%
15 583
 
4.0%
10.35 480
 
3.3%
19.8 416
 
2.8%
16.5 405
 
2.8%
20 294
 
2.0%
12.5 269
 
1.8%
13.15 234
 
1.6%
Other values (688) 6297
43.1%
ValueCountFrequency (%)
3.3 3
< 0.1%
3.464 1
 
< 0.1%
4 1
 
< 0.1%
5 2
< 0.1%
5.196 2
< 0.1%
5.75 2
< 0.1%
6 2
< 0.1%
6.9 1
 
< 0.1%
6.928 4
< 0.1%
7.7 1
 
< 0.1%
ValueCountFrequency (%)
320 1
 
< 0.1%
260 1
 
< 0.1%
200 4
< 0.1%
192 1
 
< 0.1%
180 2
< 0.1%
166 1
 
< 0.1%
164 1
 
< 0.1%
160 2
< 0.1%
155.88 1
 
< 0.1%
155 3
< 0.1%

churn
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size827.4 KiB
0
13187 
1
1419 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14606
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 13187
90.3%
1 1419
 
9.7%

Length

2023-07-08T18:12:55.840250image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-08T18:12:55.941979image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
0 13187
90.3%
1 1419
 
9.7%

Most occurring characters

ValueCountFrequency (%)
0 13187
90.3%
1 1419
 
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14606
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13187
90.3%
1 1419
 
9.7%

Most occurring scripts

ValueCountFrequency (%)
Common 14606
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13187
90.3%
1 1419
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14606
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13187
90.3%
1 1419
 
9.7%

start_year
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.5775
Minimum2003
Maximum2014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size114.2 KiB
2023-07-08T18:12:56.023790image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum2003
5-th percentile2009
Q12010
median2011
Q32012
95-th percentile2013
Maximum2014
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6532678
Coefficient of variation (CV)0.00082228506
Kurtosis3.2281209
Mean2010.5775
Median Absolute Deviation (MAD)1
Skewness-1.2368333
Sum29366495
Variance2.7332946
MonotonicityNot monotonic
2023-07-08T18:12:56.115514image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2012 3704
25.4%
2010 3609
24.7%
2011 2758
18.9%
2009 2690
18.4%
2013 1224
 
8.4%
2005 189
 
1.3%
2004 137
 
0.9%
2008 124
 
0.8%
2007 95
 
0.7%
2003 39
 
0.3%
Other values (2) 37
 
0.3%
ValueCountFrequency (%)
2003 39
 
0.3%
2004 137
 
0.9%
2005 189
 
1.3%
2006 36
 
0.2%
2007 95
 
0.7%
2008 124
 
0.8%
2009 2690
18.4%
2010 3609
24.7%
2011 2758
18.9%
2012 3704
25.4%
ValueCountFrequency (%)
2014 1
 
< 0.1%
2013 1224
 
8.4%
2012 3704
25.4%
2011 2758
18.9%
2010 3609
24.7%
2009 2690
18.4%
2008 124
 
0.8%
2007 95
 
0.7%
2006 36
 
0.2%
2005 189
 
1.3%

end_year
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size870.2 KiB
2016
13663 
2017
 
943

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters58424
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2016 13663
93.5%
2017 943
 
6.5%

Length

2023-07-08T18:12:56.217269image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-07-08T18:12:56.317974image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
2016 13663
93.5%
2017 943
 
6.5%

Most occurring characters

ValueCountFrequency (%)
2 14606
25.0%
0 14606
25.0%
1 14606
25.0%
6 13663
23.4%
7 943
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 58424
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 14606
25.0%
0 14606
25.0%
1 14606
25.0%
6 13663
23.4%
7 943
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Common 58424
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 14606
25.0%
0 14606
25.0%
1 14606
25.0%
6 13663
23.4%
7 943
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58424
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 14606
25.0%
0 14606
25.0%
1 14606
25.0%
6 13663
23.4%
7 943
 
1.6%

Interactions

2023-07-08T18:12:46.557273image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:04.793473image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:07.152365image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:09.275779image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:11.326306image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:13.643110image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:15.720554image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:17.929646image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:20.071917image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:22.509427image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:24.701563image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:27.036348image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:29.211503image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:31.430568image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:33.618740image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:36.082190image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:38.196597image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:40.280025image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:42.369437image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:44.425937image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:46.670969image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:04.914319image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:07.266062image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:09.380499image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:11.442995image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:13.748827image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:15.834250image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:18.040350image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:20.180626image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:22.619161image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:24.826230image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:27.156996image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:29.326196image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:31.542269image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:33.730416image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:36.190900image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:38.301317image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:40.386740image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:42.475155image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:44.535644image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:46.778680image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:05.020037image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:07.368810image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:09.479235image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:11.557687image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:13.851553image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:15.945951image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:18.146067image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:20.574573image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:22.726845image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:24.940924image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:27.265706image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:29.435903image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:31.649981image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:33.838144image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:36.296616image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:38.407035image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:40.490463image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:42.577880image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:44.642358image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:46.875421image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:05.119770image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:07.465529image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:09.573012image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:11.664402image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:13.946299image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:16.047679image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:18.244803image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:20.672340image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:22.838546image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:25.061600image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:27.365470image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:29.554584image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:31.752733image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:33.937877image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:36.402333image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:38.501781image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:40.586234image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:42.670632image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:44.739100image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:46.990115image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:05.235461image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:07.579224image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:09.688706image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:11.795053image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:14.058001image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:16.167360image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:18.362489image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:20.790024image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:22.959224image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:25.184272image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:27.483154image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:29.673268image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:31.871389image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:34.056561image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:36.516051image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:38.616474image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:40.698905image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:42.782359image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:44.856786image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:47.086856image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-07-08T18:12:08.760159image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:10.837614image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:13.076625image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:15.210917image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:17.395099image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:19.557294image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:21.994802image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:24.169986image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:26.482829image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:28.691892image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:30.895997image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:33.095116image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:35.563557image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:37.686960image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:39.778367image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:41.871768image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:43.929267image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:46.049594image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:48.263708image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:06.740468image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:08.866903image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:10.933357image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:13.206280image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:15.308656image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:17.499796image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:19.659021image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:22.098526image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:24.273708image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:26.594500image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:28.795615image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:30.999748image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:33.199864image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:35.666332image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:37.788689image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:39.878100image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:41.970504image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:44.027004image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:46.150360image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:48.363469image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:06.842195image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:08.966607image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:11.031096image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:13.314987image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:15.406395image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:17.606511image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:19.761748image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:22.200254image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:24.376433image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:26.704207image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:28.899364image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:31.104440image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:33.304555image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:35.770046image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:37.890444image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:39.977833image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:42.068243image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:44.125741image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:46.252113image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:48.461181image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:06.943924image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:09.066340image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:11.125842image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:13.421703image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:15.507125image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:17.714222image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:19.863475image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:22.301012image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:24.478162image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:26.812917image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:29.002062image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:31.215143image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:33.408278image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:35.872749image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:37.990149image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:40.075572image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:42.165981image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:44.223479image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:46.351822image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:48.564932image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:07.049668image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:09.172059image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:11.227570image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:13.535432image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:15.608854image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:17.823929image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:19.969192image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:22.404706image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:24.596845image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:26.925615image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:29.106782image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:31.325848image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:33.513025image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:35.978467image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:38.092875image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:40.179294image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:42.268707image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:44.325207image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-07-08T18:12:46.454546image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Correlations

2023-07-08T18:12:56.436263image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
channel_salescons_12mcons_gas_12mcons_last_monthforecast_cons_12mforecast_cons_yearforecast_discount_energyforecast_meter_rent_12mforecast_price_energy_off_peakforecast_price_energy_peakforecast_price_pow_off_peakimp_consmargin_gross_pow_elemargin_net_pow_elenb_prod_actnet_marginnum_years_antigorigin_uppow_maxstart_yearhas_gaschurnend_year
channel_sales1.0000.089-0.0110.0810.0610.0180.024-0.0170.0660.0020.0060.0290.0300.030-0.0100.052-0.3990.150-0.0140.4120.0530.0810.021
cons_12m0.0891.0000.1610.7070.6950.429-0.0240.288-0.2860.323-0.3640.411-0.094-0.0940.1190.688-0.0100.0160.3500.0320.2340.0490.062
cons_gas_12m-0.0110.1611.0000.1480.1370.0910.0050.058-0.0480.065-0.0750.093-0.026-0.0260.8650.134-0.0020.0010.0760.0050.3020.0370.058
cons_last_month0.0810.7070.1481.0000.4430.771-0.0180.351-0.2840.377-0.3700.759-0.002-0.0020.1090.4450.0090.0330.3510.0220.2310.0410.055
forecast_cons_12m0.0610.6950.1370.4431.0000.5080.0520.258-0.2490.267-0.3250.515-0.175-0.1750.1250.950-0.0600.0630.3400.0710.0250.0190.039
forecast_cons_year0.0180.4290.0910.7710.5081.0000.0140.426-0.3710.435-0.4440.987-0.011-0.0110.0720.516-0.0130.0930.4250.0310.0000.0000.037
forecast_discount_energy0.024-0.0240.005-0.0180.0520.0141.000-0.0030.2750.0900.1490.0330.2410.2400.1440.018-0.0830.0790.0030.0930.0190.0230.026
forecast_meter_rent_12m-0.0170.2880.0580.3510.2580.426-0.0031.000-0.6080.690-0.6250.3970.1160.1160.0340.2930.0060.0540.6600.0000.0580.0420.045
forecast_price_energy_off_peak0.066-0.286-0.048-0.284-0.249-0.3710.275-0.6081.000-0.4700.714-0.3210.0410.0410.012-0.325-0.035-0.109-0.6700.0530.0680.0500.136
forecast_price_energy_peak0.0020.3230.0650.3770.2670.4350.0900.690-0.4701.000-0.7310.4000.1760.1760.0650.326-0.0170.0270.7060.0210.0610.0410.000
forecast_price_pow_off_peak0.006-0.364-0.075-0.370-0.325-0.4440.149-0.6250.714-0.7311.000-0.392-0.139-0.140-0.030-0.4230.003-0.090-0.715-0.0530.0690.0550.129
imp_cons0.0290.4110.0930.7590.5150.9870.0330.397-0.3210.400-0.3921.000-0.017-0.0170.0770.501-0.0290.0940.3870.0470.0210.0000.026
margin_gross_pow_ele0.030-0.094-0.026-0.002-0.175-0.0110.2410.1160.0410.176-0.139-0.0171.0001.000-0.001-0.119-0.0410.0630.2630.1890.0190.0890.109
margin_net_pow_ele0.030-0.094-0.026-0.002-0.175-0.0110.2400.1160.0410.176-0.140-0.0171.0001.000-0.001-0.119-0.0410.0630.2630.1890.0190.0890.109
nb_prod_act-0.0100.1190.8650.1090.1250.0720.1440.0340.0120.065-0.0300.077-0.001-0.0011.0000.108-0.0130.0170.0480.0140.1150.0000.031
net_margin0.0520.6880.1340.4450.9500.5160.0180.293-0.3250.326-0.4230.501-0.119-0.1190.1081.000-0.0560.0860.4060.0770.0170.0270.000
num_years_antig-0.399-0.010-0.0020.009-0.060-0.013-0.0830.006-0.035-0.0170.003-0.029-0.041-0.041-0.013-0.0561.000-0.414-0.026-0.9330.0430.0870.151
origin_up0.1500.0160.0010.0330.0630.0930.0790.054-0.1090.027-0.0900.0940.0630.0630.0170.086-0.4141.0000.1140.4280.0020.0970.010
pow_max-0.0140.3500.0760.3510.3400.4250.0030.660-0.6700.706-0.7150.3870.2630.2630.0480.406-0.0260.1141.0000.0320.0300.0260.010
start_year0.4120.0320.0050.0220.0710.0310.0930.0000.0530.021-0.0530.0470.1890.1890.0140.077-0.9330.4280.0321.0000.0570.0890.198
has_gas0.0530.2340.3020.2310.0250.0000.0190.0580.0680.0610.0690.0210.0190.0190.1150.0170.0430.0020.0300.0571.0000.0230.000
churn0.0810.0490.0370.0410.0190.0000.0230.0420.0500.0410.0550.0000.0890.0890.0000.0270.0870.0970.0260.0890.0231.0000.001
end_year0.0210.0620.0580.0550.0390.0370.0260.0450.1360.0000.1290.0260.1090.1090.0310.0000.1510.0100.0100.1980.0000.0011.000

Missing values

2023-07-08T18:12:48.757415image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-07-08T18:12:49.183276image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

idchannel_salescons_12mcons_gas_12mcons_last_monthdate_activdate_enddate_modif_proddate_renewalforecast_cons_12mforecast_cons_yearforecast_discount_energyforecast_meter_rent_12mforecast_price_energy_off_peakforecast_price_energy_peakforecast_price_pow_off_peakhas_gasimp_consmargin_gross_pow_elemargin_net_pow_elenb_prod_actnet_marginnum_years_antigorigin_uppow_maxchurnstart_yearend_year
024011ae4ebbe3035111d65fa7c15bc57405494602013-06-152016-06-152015-11-012015-06-230.0000.01.780.1144810.09814240.606701t0.0025.4425.442678.993443.648120132016
1d29c2c54acc38ff3c0614d0a653813dd04660002009-08-212016-08-302009-08-212015-08-31189.9500.016.270.1457110.00000044.311378f0.0016.3816.38118.896213.800020092016
2764c75f661154dac3a6c254cd082ea7d4544002010-04-162016-04-162010-04-162015-04-1747.9600.038.720.1657940.08789944.311378f0.0028.6028.6016.606213.856020102016
3bba03439a292a1e166f80264c16191cb51584002010-03-302016-03-302010-03-302015-03-31240.0400.019.830.1466940.00000044.311378f0.0030.2230.22125.466213.200020102016
4149d57cf92fc41cf94415803a877cb4b0442505262010-01-132016-03-072010-01-132015-03-09445.755260.0131.730.1169000.10001540.606701f52.3244.9144.91147.986219.800020102016
51aa498825382410b098937d65c4ec26d78302019982011-12-092016-12-092015-11-012015-12-10796.9419980.030.120.1647750.08613145.308378f181.2133.1233.121118.894413.200120112016
67ab4bf4878d8f7661dfc20e9b8e18011445097002011-12-022016-12-022011-12-022015-12-038069.2800.00.000.1661780.08753844.311378f0.004.044.041346.634415.000120112016
701495c955be7ec5e7f3203406785aae0429552012602010-04-212016-04-212010-04-212015-04-22864.737510.0144.490.1151740.09883740.606701f70.6353.9253.921100.096426.400020102016
8f53a254b1115634330c12c7fdbf7958a72962002011-09-232016-09-232011-09-232015-09-25444.3800.015.850.1457110.00000044.311378f0.0012.8212.82142.594213.200020112016
910c1b2f97a2d2a6f10299dc213d1a370526064021882010-05-042016-05-042015-04-292015-05-052738.1021880.0130.430.1157610.09941940.606701f219.5933.4233.421329.606431.500020102016
idchannel_salescons_12mcons_gas_12mcons_last_monthdate_activdate_enddate_modif_proddate_renewalforecast_cons_12mforecast_cons_yearforecast_discount_energyforecast_meter_rent_12mforecast_price_energy_off_peakforecast_price_energy_peakforecast_price_pow_off_peakhas_gasimp_consmargin_gross_pow_elemargin_net_pow_elenb_prod_actnet_marginnum_years_antigorigin_uppow_maxchurnstart_yearend_year
14596c3f4f737d598a1b47a94440bb18c3c0651097002011-02-092016-02-092011-02-092015-02-11165.6000.016.040.1466940.00000044.311378f0.0026.0426.04117.385310.392020112016
14597ae818f3cc00ef5845416699aacc4bd7e2831006852012-12-182016-12-182012-12-182015-12-21833.056850.0131.760.1152370.10012340.939027f67.0324.0224.021102.523223.100020122016
145981582ef35fbfa265e60bb3399bdebac870944104802009-10-082016-10-082015-05-242015-10-09983.974800.0132.110.1152370.10012340.939027f46.9820.0020.001113.176315.001020092016
1459946362cb1ad2fcdad347a6fa1bc1e5d4b418163303602010-01-262017-01-262015-11-172016-01-272663.8200.016.350.1435750.00000044.311378t0.0031.2031.203254.816213.856020102017
14600c49217f16a06263e5381eaba94a67a8b4871460113672013-02-082016-02-082013-02-082015-02-09712.337130.0145.820.1203720.10348740.606701f71.8166.0066.00187.143426.400020132016
1460118463073fb097fc0ac5d3e040f3569874322704794002012-05-242016-05-082015-05-082014-05-264648.0100.018.570.1383050.00000044.311378t0.0027.8827.882381.774415.000020122016
14602d0a6f71671571ed83b2645d23af6de004722301812012-08-272016-08-272012-08-272015-08-28631.691810.0144.030.1001670.09189258.995952f15.940.000.00190.34346.000120122016
1460310e6828ddd62cbcf687cb74928c4c2d24184401792012-02-082016-02-072012-02-082015-02-09190.391790.0129.600.1169000.10001540.606701f18.0539.8439.84120.384415.935120122016
146041cf20fd6206d7678d5bcafd28c53b4db4131002012-08-302016-08-302012-08-302015-08-3119.3400.07.180.1457110.00000044.311378f0.0013.0813.0810.963411.000020122016
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